Research and System Design of Digital Education Mechanism for Vocational Education in Cloud Computing Environment

This study aims to address the core issues of vocational education in the current cloud computing environment, such as the curriculum system lagging behind technological development and the lack of dynamic data support for talent skill assessment. It aims to build a digital education mechanism and system platform based on cloud computing and artificial intelligence technology, and achieve accurate matching of vocational education with industry needs by establishing an education model of "data-driven-intelligent adaptation-dynamic feedback". First, crawler technology is used to integrate 46,000 cloud computing job recruitment data and 32 vocational college teaching data to construct a job capability knowledge graph; secondly, learner portraits are classified based on the K-means clustering algorithm, and a dynamic evaluation system containing 8 dimensions and 64 indicators is established; then a system architecture with three core functions of intelligent course recommendation, virtual training environment, and real-time capability assessment is designed, and an LSTM neural network is introduced for learning behavior prediction. Within a 12-month period of three pilot colleges, the system enables learners to achieve a job adaptation rate of 85.2%, a mean resource allocation delay of 9.24ms, and an employment rate of 92.5%, which verifies the remarkable effect of this mechanism in improving the adaptability of vocational education and provides a replicable technical path for vocational education reform under the background of digital transformation.